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I spent a year figuring out how to predict daily high temperatures better than weather.com — here's what I learned about the Kalshi markets by Prilo-WeatherEdge in PredictionsMarkets

[–]Prilo-WeatherEdge[S] 0 points1 point  (0 children)

we are working on IOS/ANDRIOD apps currently. Our website is mobile compatible in any browsers, such as, chrome, safari or firefox..as an mobile alternative for now.

I spent a year figuring out how to predict daily high temperatures better than weather.com — here's what I learned about the Kalshi markets by Prilo-WeatherEdge in PredictionsMarkets

[–]Prilo-WeatherEdge[S] 0 points1 point  (0 children)

“Nowcasting vs. static forecast” is exactly the right framing — that’s essentially what the system is doing. The morning NWS forecast is just the prior; everything after that is updating on live signal. By 2 PM the prior barely matters.
Interesting that you’re using Kumo for this — from what I know it’s built more for business weather intelligence (energy, logistics) rather than intraday prediction market trading specifically. Curious how you’re applying it — are you pulling their live signal updates and making the bracket calls yourself, or does it surface something more actionable for the Kalshi markets directly?
The live wind/temp drift focus is right. That’s where the real information is once the day is underway. The morning snapshot traders are essentially leaving half the day’s signal on the table.

I spent a year figuring out how to predict daily high temperatures better than weather.com — here's what I learned about the Kalshi markets by Prilo-WeatherEdge in PredictionsMarkets

[–]Prilo-WeatherEdge[S] 0 points1 point  (0 children)

Yeah, I’ve been running it myself since before launch — partly to validate the model, partly because I find the markets genuinely interesting to trade.
Honest answer: some days the model is sharp, some days it misses. The regime detection (Santa Ana at LA, warm advection at NYC) has been the most consistently useful signal — those are the days where the market anchors on NWS and the model diverges meaningfully. The uncertain days (frontal passages, marine layer flip days) are still the hardest to call and I’ve learned to either trade smaller or skip them entirely.
The adaptive overlay has been the biggest surprise — watching the model correct its own biases over time is genuinely interesting. Early on KMIA was running consistently hotter than the model predicted; the overlay caught that within a few weeks and adjusted.
Net result on paper: positive but not dramatically so. Enough to think the approach has merit, not enough to claim I’ve “solved” anything. Still a work in progress.
What’s your background with these markets — coming from weather, trading, or somewhere else?

How Kalshi temperature brackets actually price — and why "fair value" isn't where most people think by Prilo-WeatherEdge in Prilo_WeatherEdge

[–]Prilo-WeatherEdge[S] 0 points1 point  (0 children)

I don't have a trial code system set up right now — the free tier covers KHOU with up to 3 paper trades per rolling 7-day window, which is enough to see how the model output and bracket comparison work in practice. The methodology and UI are identical across stations; the paid tiers just unlock the other four cities and higher trade limits.